Optimal Multiuser On-Street Parking Allocation

The conflict between the increased parking demand and limited curbside space calls for efficient on-street parking management. Among various parking demands, unsatisfied freight and service vehicles could cause high societal costs. Research has found that these two types of vehicles have major differences in parking duration, frequency, and required space. In addition, for delivery convenience, freight vehicles are more likely to double park when on-street parking space is unavailable, causing severe congestion and safety concerns, while the shortage of on-street parking space for service vehicles could cause significant social impacts. These observations signify the need to consider user priority when allocating the limited on-street parking spaces. However, research on on-street parking allocation involving freight and service vehicles receives little attention without recognizing the differences in their parking activities. To this end, this paper proposes an optimization model to optimally allocate on-street parking space to multiple users, including passenger cars, freight vehicles, and service vehicles, considering their distinct features and impacts. Parking activities of these users are modeled based on queuing theory with random arrival and random parking duration, allowing agencies to evaluate and warrant the parking availability for prioritized vehicles. The proposed model also optimizes the parking time limit on passenger cars to improve parking space usage. The case study shows that the optimal solution effectively prioritizes parking spaces for freight and service vehicles due to their significant economic impacts on society. To minimize the overall social cost, the optimal solution will allocate parking spaces to passenger cars only when their expected parking duration is short enough. Setting a proper parking time limit for cars can help filter out the cars with a longer parking duration, allowing the on-street parking spaces to serve more vehicles efficiently